18-04-2012, 04:07 PM
Speech and Language Processing
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Introduction
Some of these, such as definition questions, or simple factoid questions like dates
and locations, can already be answered by search engines. But answering more complicated
questions might require extracting information that is embedded in other text
on a Web page, doing inference (drawing conclusions based on known facts), or synthesizing
and summarizing information from multiple sources or Web pages. In this
text we study the various components that make up modern understanding systems of
this kind, including information extraction, word sense disambiguation, and so on.
Although the subfields and problems we’ve described above are all very far from
completely solved, these are all very active research areas and many technologies are
already available commercially. In the rest of this chapter, we briefly summarize the
kinds of knowledge that are necessary for these tasks (and others like spelling correction,
grammar checking, and so on), as well as the mathematical models that are
introduced throughout the book.
Knowledge in Speech and Language Processing
What distinguishes language processing applications from other data processing systems
is their use of knowledge of language. Consider the Unix wc program, which
counts the total number of bytes, words, and lines in a text file. When used to count
bytes and lines, wc is an ordinary data processing application. However, when it is
used to count the words in a file, it requires knowledge about what it means to be a
word and thus becomes a language processing system.
Of course, wc is an extremely simple system with an extremely limited and impoverished
knowledge of language. Sophisticated conversational agents like HAL, machine
translation systems, or robust question-answering systems require much broader
and deeper knowledge of language. To get a feeling for the scope and kind of required
knowledge, consider some of what HAL would need to know to engage in the dialogue
that begins this chapter, or for a question-answering system to answer one of the
questions above.
Models and Algorithms
One of the key insights of the last 50 years of research in language processing is that
the various kinds of knowledge described in the last sections can be captured through
the use of a small number of formal models or theories. Fortunately, these models and
theories are all drawn from the standard toolkits of computer science, mathematics, and
linguistics and should be generally familiar to those trained in those fields. Among the
most important models are state machines, rule systems, logic, probabilistic models,
and vector-space models. These models, in turn, lend themselves to a small number
of algorithms, among the most important of which are state space search algorithms,
such as dynamic programming, and machine learning algorithms, such as classifiers
and Expectation-Maximization (EM) and other learning algorithms.
In their simplest formulation, state machines are formal models that consist of
states, transitions among states, and an input representation. Some of the variations
of this basic model that we will consider are deterministic and non-deterministic
finite-state automata and finite-state transducers.
The State of the Art
We can only see a short distance ahead, but we can see plenty there that needs to be done.
Alan Turing
This is an exciting time for the field of speech and language processing. The startling
increase in computing resources available to the average computer user, the rise of
the Web as a massive source of information, and the increasing availability of wireless
mobile access have all placed speech- and language-processing applications in the
technology spotlight. The following are examples of some currently deployed systems
that reflect this trend:
• Travelers calling Amtrak, United Airlines, and other travel providers interact
with conversational agents that guide them through the process of making reservations
and getting arrival and departure information.
• Car makers provide automatic speech recognition and text-to-speech systems
that allow drivers to control their environmental, entertainment, and navigational
systems by voice. A similar spoken dialogue system has been deployed by astronauts
on the International Space Station.
Some Brief History
Historically, speech and language processing has been treated very differently in computer
science, electrical engineering, linguistics, and psychology/cognitive science.
Because of this diversity, speech and language processing encompasses a number of
different but overlapping fields in these different departments: computational linguistics
in linguistics, natural language processing in computer science, speech recognition
in electrical engineering, computational psycholinguistics in psychology. This
section summarizes the different historical threads that have given rise to the field of
speech and language processing. This section provides only a sketch, but many of the
topics listed here are covered in more detail in subsequent chapters.
Summary
Ogburn and Thomas are generally credited with noticing that the prevalence of multiple inventions suggests
that the cultural milieu and not individual genius is the deciding causal factor in scientific discovery. In
an amusing bit of recursion, however, Merton notes that even this idea has been multiply discovered, citing
sources from the 19th century and earlier!